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The HIV capsid (CA) protein is a promising target for anti‐AIDS treatment due to its critical involvement in viral replication. Herein, we utilized the well‐documented CA inhibitor PF74 as our lead compound and designed a series of low‐molecular‐weight phenylalanine derivatives. Among them, compound 7t exhibited remarkable antiviral activity with a high selection index (EC50 = 0.040 µM, SI = 2815), surpassing that of PF74 (EC50 = 0.50 µM, SI = 258). Furthermore, when evaluated against the HIV‐2 strain, 7t (EC50 = 0.13 µM) demonstrated approximately 14‐fold higher potency than that of PF74 (EC50 = 1.76 µM). Insights obtained from surface plasmon resonance (SPR) revealed that 7t exhibited stronger target affinity to the CA hexamer and monomer in comparison to PF74. The potential interactions between 7t and the HIV‐1 CA were further elucidated using molecular docking and molecular dynamics simulations, providing a plausible explanation for the enhanced target affinity with 7t over PF74. Moreover, the metabolic stability assay demonstrated that 7t (T1/2 = 77.0 min) significantly outperforms PF74 (T1/2 = 0.7 min) in human liver microsome, exhibiting an improvement factor of 110‐fold. In conclusion, 7t emerges as a promising drug candidate warranting further investigation. [ABSTRACT FROM AUTHOR]

The new high selective mAChRs M3 inhibitors with IC50 in nanomolecular ranges, which can be the prototypes for effective COPD and asthma treatment drugs, were discovered with computational approaches among trifluoromethyl containing hexahydropyrimidinones/thiones. Compounds [6‐(4‐ethoxy‐3‐methoxy‐phenyl)‐4‐hydroxy‐2‐thioxo‐4‐(trifluoromethyl)hexahydropyrimidin‐5‐yl]‐phenyl‐methanone (THPT‐1) and 5‐benzoyl‐6‐(3,4‐dimethoxyphenyl)‐4‐hydroxy‐4‐(trifluoromethyl)hexahydropyrimidin‐2‐one (THPO‐4) have been proved to be a highly effective (with IC50 values of 1.62 ⋅ 10−7 M and 3.09 ⋅ 10−9 M, respectively) at the same concentrations significantly competitive inhibit the signal conduction through mAChR3 in comparison with ipratropium bromide, without significant effect on mAChR2, nicotinic cholinergic and adrenergic receptors. [ABSTRACT FROM AUTHOR]

NFX1‐123 is a splice variant isoform of the NFX1 gene. It is highly expressed in cervical cancers caused by HPV, and NFX1‐123 is a protein partner with the HPV oncoprotein E6. Together, NFX1‐123 and E6 affect cellular growth, longevity, and differentiation. The expression status of NFX1‐123 in cancers beyond cervical and head and neck cancers, and its potential as therapeutic target, have not been investigated. TSVdb of TCGA was used to quantify NFX1‐123 expression in 24 cancers compared with normal tissues. The NFX1‐123 protein structure was predicted and then submitted to retrieve suitable drug molecules. The top four compounds, found to bind in silico to NFX1‐123, were tested experimentally to determine their effects on NFX1‐123‐related cellular growth, survival, and migration. 46% of cancers (11 of 24 had significant differences in NFX1‐123 expression, with nine having had greater NFX1‐123 expression, when compared with adjacent normal tissues. Bioinformatics and proteomic predictive analysis modeled the three‐dimensional structure of NFX1‐123, and drug libraries were screened for high‐binding affinity compounds using this modeled structure. Seventeen drugs with binding energies ranging from −1.3 to −10 Kcal/mol were identified. The top four compounds were used to treat HPV‐ and HPV+ cervical cancer cell lines, three of which (Ropitoin, R428 and Ketoconazole) reduced NFX1‐123 protein levels, inhibited cellular growth, survival, and migration, and enhanced the cytotoxicity of Cisplatin. These findings highlight cancers expressing high levels of NFX1‐123, and drugs that target it, may reduce cellular growth, survival, and migration, making NFX1‐123 a potential novel therapeutic target. [ABSTRACT FROM AUTHOR]

We report a series of hybrid oxoazetidine conjugated thiazoles as epidermal growth factor receptor (EGFR) inhibitors, which were synthesized and tested using a variety of in silico and in vitro studies. The compounds were found to be active against breast and hepatic cancer cell lines, with Compounds 7a, 7b, and 7e being the most potent ones. The derivatives were also evaluated for molecular docking and complementarity studies to explicate fundamental substituent groups essential for their bioactivity. Moreover, the structural activity relationship of the analogues was performed for future compound optimization. These studies advocated that the analogues have a high affinity towards EGFR with favorable anticancer potential. The study advised that the derivatives have potency against breast and hepatic cancer and can assist as an initial scaffold for further development of anti‐EGFR compounds. [ABSTRACT FROM AUTHOR]

Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo drug discovery is time consuming and expensive process with high failure rate in clinical stages. To address this problem and provide treatments to patients suffering from viral cancers faster, drug repurposing emerges as an effective alternative which aims to find the other indications of the Food and Drug Administration approved drugs. Applied to viral cancers, drug repurposing studies following the niche have tried to find if already existing drugs could be used to treat viral cancers. Multiple drug repurposing approaches till date have been introduced with successful results in viral cancers and many drugs have been successfully repurposed various viral cancers. Here in this study, a critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening‐based drug repurposing studies focusing on viral cancers is provided. Additionally, the mechanism of viral cancers is presented along with drug repurposing case study specific to each viral cancer. Finally, the limitations and challenges of various approaches along with possible solutions are provided. [ABSTRACT FROM AUTHOR]

In this study, we capitalized on our previously performed crystallographic fragment screen and developed the antitubulin small molecule Todalam with only two rounds of straightforward chemical synthesis. Todalam binds to a novel tubulin site, disrupts microtubule networks in cells, arrests cells in G2/M, induces cell death, and synergizes with vinblastine. The compound destabilizes microtubules by acting as a molecular plug that sterically inhibits the curved‐to‐straight conformational switch in the α‐tubulin subunit, and by sequestering tubulin dimers into assembly incompetent oligomers. Our results describe for the first time the generation of a fully rationally designed small molecule tubulin inhibitor from a fragment, which displays a unique molecular mechanism of action. They thus demonstrate the usefulness of tubulin‐binding fragments as valuable starting points for innovative antitubulin drug and chemical probe discovery campaigns. [ABSTRACT FROM AUTHOR]

Two decades of research have proven the relevance of ion channel expression for tumor progression in virtually every indication, and it has become clear that inhibition of specific ion channels will eventually become part of the oncology therapeutic arsenal. However, ion channels play relevant roles in all aspects of physiology, and specificity for the tumor tissue remains a challenge to avoid undesired effects. Eag1 (KV10.1) is a voltage‐gated potassium channel whose expression is very restricted in healthy tissues outside of the brain, while it is overexpressed in 70% of human tumors. Inhibition of Eag1 reduces tumor growth, but the search for potent inhibitors for tumor therapy suffers from the structural similarities with the cardiac HERG channel, a major off‐target. Existing inhibitors show low specificity between the two channels, and screenings for Eag1 binders are prone to enrichment in compounds that also bind HERG. Rational drug design requires knowledge of the structure of the target and the understanding of structure–function relationships. Recent studies have shown subtle structural differences between Eag1 and HERG channels with profound functional impact. Thus, although both targets' structure is likely too similar to identify leads that exclusively bind to one of the channels, the structural information combined with the new knowledge of the functional relevance of particular residues or areas suggests the possibility of selective targeting of Eag1 in cancer therapies. Further development of selective Eag1 inhibitors can lead to first‐in‐class compounds for the treatment of different cancers. [ABSTRACT FROM AUTHOR]

The growth of the global population poses a huge challenge to food security. The continuous application of crop protection products has many negative effects including increased pesticide resistance, accumulation of residues, and ecological damage. The challenge for the future is to develop agrochemicals with that are effective, with a high degree of selectivity, environmental friendliness, a low use rate, and cost‐effectiveness. Cutting‐edge developments in natural product chemistry, asymmetric synthesis, and rational drug design, as well as an increasing knowledge of the mechanisms that induce plant immunity, have provided potential solutions. In this review, we discuss the comprehensive use of emerging technologies that will help to tackle these current challenges and issues, highlighting the discovery of novel crop production products and their beneficial properties. [ABSTRACT FROM AUTHOR]

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as "Post‐translational Modification Inspired Drug Design (PTMI‐DD)." PTMI‐DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI‐DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild‐type counterpart, targeting protein‐protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms. [ABSTRACT FROM AUTHOR]

African trypanosomiasis, sleeping sickness in humans or nagana in animals, is a potentially fatal neglected tropical disease and a threat to 65 million human lives and 100 million small and large livestock animals in sub-Saharan Africa. Available treatments for this devastating disease are few and have limited efficacy, prompting the search for new drug candidates. Simultaneous inhibition of the trypanosomal glycerol kinase (TGK) and trypanosomal alternative oxidase (TAO) is considered a validated strategy toward the development of new drugs. Our goal is to develop a TGK-specific inhibitor for coadministration with ascofuranone (AF), the most potent TAO inhibitor. Here, we report on the identification of novel compounds with inhibitory potency against TGK. Importantly, one of these compounds (compound 17) and its derivatives (17a and 17b) killed trypanosomes even in the absence of AF. Inhibition kinetics revealed that derivative 17b is a mixed-type and competitive inhibitor for TGK and TAO, respectively. Structural data revealed the molecular basis of this dual inhibitory action, which, in our opinion, will aid in the successful development of a promising drug to treat trypanosomiasis. Although the EC50 of compound 17b against trypanosome cells was 1.77 µM, it had no effect on cultured human cells, even at 50 µM. [ABSTRACT FROM AUTHOR]

Understanding the way in which drugs are metabolized by cytochrome P450 2D6 (CYP2D6) and hence the underlying mechanisms that define potential toxicity is crucial to avoid adverse reactions. The high occurrence of CYP2D6 polymorphs enhances the complexity of the toxicity assessment of a drug candidate and should be tackled from early drug discovery phase on. The recent increase in available mammalian CYP2D6 X‐ray structures opens the gateway to the development of in silico three‐dimensional CYP2D6 toxicity prediction techniques that address also the major clinically relevant allelic variants. This review presents the basic principles needed for comprehending the enzyme particularities, gives a concise overview of several clinical relevant allelic CYP2D6 variants, and explores the state‐of‐the‐art CYP2D6 research field to accelerate the development and use of such integrative in silico modeling technologies. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Molecular Structures Software > Molecular Modeling [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA), as a well-known multivariate data analysis and data reduction technique, is an important and useful algebraic tool in drug design and discovery. PCA, in a typical quantitative structure-activity relationship (QSAR) study, analyzes an original data matrix in which molecules are described by several intercorrelated quantitative dependent variables (molecular descriptors). Although extensively applied, there is disparity in the literature with respect to the applications of PCA in the QSAR studies. This study investigates the different applications of PCA in QSAR studies using a dataset including CCR5 inhibitors. The different types of preprocessing are used to compare the PCA performances. The use of PC plots in the exploratory investigation of matrix of descriptors is described. This work is also proved PCA analysis to be a powerful technique for exploring complex datasets in QSAR studies for identification of outliers. This study shows that PCA is able to easily apply to the pool of calculated structural descriptors and also the extracted information can be used to help decide upon an appropriate harder model for further analysis. [ABSTRACT FROM AUTHOR]

A series of novel indazole-based diarylurea derivatives targeting c-kit were designed by structure-based drug design. The derivatives were prepared, and their antiproliferative activities were evaluated against human colon cancer HCT-116 cell line and hepatocellular carcinoma PLC/PRF/5 cell line. The antiproliferative activities demonstrated that six of nine compounds exhibited comparable activities with sorafenib against HCT-116. The structure-activity relationship (SAR) analysis indicated that the indazole ring part tolerated different kinds of substituents, and the N position of the central pyridine ring played key roles in antiproliferative activity. The SAR and interaction mechanisms were further explored using molecular docking method. Compound 1i with N-(2-(pyrrolidin-1-yl)ethyl)-carboxamide possessed improved solubility, 596.1 ng/ml and best activities, IC50 at 1.0 μ m against HCT-116, and 3.48 μ m against PLC/PRF/5. It is a promising anticancer agent for further development. [ABSTRACT FROM AUTHOR]

The physiological role played by protein-ligand recognition has motivated the development of several computational approaches to the ligand binding affinity. Some of them, termed rigorous, have a strong theoretical foundation but involve too much computation to be generally useful. Some others alleviate the computational burden by introducing strong approximations and/or empirical calibrations, which also limit their general use. Most importantly, there is no straightforward correlation between the predictive power and the level of approximation introduced. Here, we present a general framework for the quantitative interpretation of protein-ligand binding based on statistical mechanics. Within this framework, we re-derive self-consistently the fundamental equations of some popular approaches to the binding constant and pinpoint the inherent approximations. Our analysis represents a first step towards the development of variants with optimum accuracy/efficiency ratio for each stage of the drug discovery pipeline. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides have long been raised as a promising strategy to combat bacterial infection in burn wounds. Here, we attempted to rationally design small antimicrobial peptides containing unnatural amino acids by integrating in silico analysis and in vitro assay. Predictive quantitative sequence-activity models were established and validated rigorously based on a large panel of nonamer antimicrobial peptides with known antibacterial activity. The best quantitative sequence-activity model predictor was employed to guide genetic evolution of a peptide population. In the evolution procedure, a number of unnatural amino acids with desired physicochemical properties were introduced, resulting in a genetic evolution-improved population, from which seven peptide candidates with top scores, containing 1-3 unnatural amino acids, and having diverse structures were successfully identified, and their antibacterial potencies against two antibiotic-resistant bacterial strains isolated from infected burn wounds were measured using in vitro susceptibility test. Consequently, four (WL-Orn-LARKIV-NH2, ARKRWF-Dab-FL-NH2, KFI-Hag-IWR-Orn-R-NH2 and YW-Hag-R-Cit-RF-Orn-N-NH2) of the seven tested peptides were found to be more potent than reference Bac2A, the smallest naturally occurring broad spectrum antimicrobial peptide. Molecular dynamics simulations revealed that the designed peptides can fold into amphipathic helical structure that allows them to interact directly with microbial membranes. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations probe the origins of aberrant functionality of R175H p53, which normally prevent tumorigenesis. This hotspot mutation exhibits loss of its essential zinc cofactor, aggregation, and activation of gain of function promoters, characteristics contributing to the loss of normal p53 activity. This study provided molecular level insight into the reorganization of the hydrogen bonding network and the formation of a hydrophobic patch on the surface of the protein. The hydrogen bonding network globally redistributes at the expense of the stability of the β-sandwich structure, and surface residues reorganize to expose a 250 Å 2 hydrophobic patch of residues covering approximately 2% of the solvent accessible surface. These changes could both stabilize the protein in the conformation exposing the patch to solvent to mediate the reported aggregation, and cause a destabilization in the area associated with DNA binding residues to affect the specificity. The development of the patch prior to loss of zinc indicates that stabilizing the patch quickly may prevent zinc loss. Considerations for rational design of small molecule therapeutics in light of the structural insight has been discussed and it suggest the positive ring around the hydrophobic patch and conserved residues may constitute a druggable site. [ABSTRACT FROM AUTHOR]

The clinically approved taxanes (paclitaxel, docetaxel and cabazitaxel) target the tubulin protein in microtubules. Despite the clinical success of these agents, the mechanism of action of this class of drugs remains elusive, making rational design of taxanes difficult. Molecular dynamics simulations of these three taxanes with the αβ-tubulin heterodimer examine the similarities and differences in the effects of the drugs on tubulin, probing both local and allosteric effects. Despite their structural similarity, the drugs adopt different conformations in the binding site on β-tubulin. The taxanes similarly increase the helical character of α- and β-tubulins. No correlations are found between microtubule assembly and (i) binding affinity or (ii) the role of the M-loop in enhancing lateral contacts. Instead, changes in intra- and interdimer longitudinal contacts are indicative of the mechanism of action of the taxanes. We find β:H1-S1′, and more importantly β:H9 and β:H10, play a role translating the effect of local drug binding in β-tubulin to an allosteric effect in α-tubulin and propose that the displacement of these secondary structures towards α-tubulin may be used as a predictor of the effect of taxanes on the tubulin heterodimers in rational drug design approaches. [ABSTRACT FROM AUTHOR]

ABSTRACT Peptides and proteins are attractive initial leads for the rational design of bioactive molecules. Several natural cyclic peptides have recently emerged as templates for drug design due to their resistance to chemical or enzymatic hydrolysis and high selectivity to receptors. The development of practical protocols that mimic the power of nature's strategies remains paramount for the advancement of novel peptide-based drugs. The de novo design of peptide mimetics (nonpeptide molecules or cyclic peptides) for the synthesis of linear or cyclic peptides has enhanced the progress of therapeutics and diverse areas of science and technology. In the case of metabolically unstable peptide ligands, the rational design and synthesis of cyclic peptide analogues has turned into an alternative approach for improved biological activity. © 2015 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 104: 453-461, 2015. [ABSTRACT FROM AUTHOR]

The CX bond in halobenzenes (XCl, Br) exhibits a dual character, being electron-deficient along the CX direction, and electron-rich on its flanks. We sought to amplify both features by resorting to electron-withdrawing and electron-donating substituents, respectively. This was done by quantum chemistry (QC) computations in the recognition sites of three protein targets: farnesyl transferase, coagulation factor Xa, and the HIV-1 integrase. In this context, some substituents, notably fluorine, CF3, and NHCH3, afforded significant overall gains in the binding energies as compared to the parent halobenzene, in the 2-5 kcal/mol range. In fact, we found that some di- and up to tetra-substitutions enabled even larger gains than those they contribute separately owing to many-body effects. Moreover, desolvation was also found to be a key contributor to the energy balances. As a consequence, some particular substituents, contributing to reduce the halobenzene dipole moment, accordingly reduced solvation: this factor acted in synergy with their enhancement of the intermolecular interaction energies along and around the CX bond. We could thus leverage the 'Janus-like' properties of such a bond and the fact that it can be tuned and possibly amplified by well-chosen substituents. We propose a simple yet rigorous computational strategy resorting to QC to prescreen novel substituted halobenzenes. The QC results on the recognition sites then set benchmarks to validate polarizable molecular mechanics/dynamics approaches used to handle the entirety of the inhibitor-protein complex. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: The serine/threonine checkpoint kinase 2 (Chk2) is an attractive molecular target for the development of small molecule inhibitors to treat cancer. Here, we report the rational design of Chk2 inhibitors that target the gatekeeper-dependent hydrophobic pocket located behind the adenine-binding region of the ATP-binding site. These compounds exhibit IC50 values in the low nanomolar range and are highly selective for Chk2 over Chk1. X-ray crystallography was used to determine the structures of the inhibitors in complex with the catalytic kinase domain of Chk2 to verify their modes of binding. [Copyright &y& Elsevier]

Microtubules are the primary target for many anti-cancer drugs, the majority of which bind specifically to β-tubulin. The existence of several β-tubulin isotypes, coupled with their varied expression in normal and cancerous cells provides a platform upon which to construct selective chemotherapeutic agents. We have examined five prevalent human β-tubulin isotypes and identified the colchicine-binding site as the most promising for drug design based on specificity. Using this binding site as a template, we have designed several colchicine derivatives and computationally probed them for affinity to the β-tubulin isotypes. These compounds were synthesized and subjected to cytotoxicity assays to determine their effectiveness against several cancerous cell lines. We observed a correlation between computational-binding predictions and experimentally determined IC50 values, demonstrating the utility of computational screening in the design of more effective colchicine derivatives. The most promising derivative exhibited an IC50 approximately threefold lower than values previously reported for either colchicine or paclitaxel, demonstrating the utility of computational design and assessment of binding to tubulin. [ABSTRACT FROM AUTHOR]

Molecular docking softwares are one of the important tools of modern drug development pipelines. The promising achievements of the last 10 years emphasize the need for further improvement, as reflected by several recent publications (Leach et al., J Med Chem 2006, 49, 5851; Warren et al., J Med Chem 2006, 49, 5912). Our initial approach, EADock, showed a good performance in reproducing the experimental binding modes for a set of 37 different ligand–protein complexes (Grosdidier et al., Proteins 2007, 67, 1010). This article presents recent improvements regarding the scoring and sampling aspects over the initial implementation, as well as a new seeding procedure based on the detection of cavities, opening the door to blind docking with EADock. These enhancements were validated on 260 complexes taken from the high quality Ligand Protein Database [LPDB, (Roche et al., J Med Chem 2001, 44, 3592)]. Two issues were identified: first, the quality of the initial structures cannot be assumed and a manual inspection and/or a search in the literature are likely to be required to achieve the best performance. Second the description of interactions involving metal ions still has to be improved. Nonetheless, a remarkable success rate of 65% was achieved for a large scale blind docking assay, when considering only the top ranked binding mode and a success threshold of 2 Å RMSD to the crystal structure. When looking at the five-top ranked binding modes, the success rate increases up to 76%. In a standard local docking assay, success rates of 75 and 83% were obtained, considering only the top ranked binding mode, or the five top binding modes, respectively. © 2009 Wiley Periodicals, Inc. J Comput Chem 2009 [ABSTRACT FROM AUTHOR]

Due in part to the increasing availability of crystallographic protein structures as well as rapid improvements in computing power, the past few decades have seen an explosion in the field of computer-based rational drug design. Several algorithms have been developed to identify or generate potential ligands in silico by optimizing the ligand–receptor hydrogen bond, electrostatic, and hydrophobic interactions. We here present AutoGrow, a novel computer-aided drug design algorithm that combines the strengths of both fragment-based growing and docking algorithms. To validate AutoGrow, we recreate three crystallographically resolved ligands from their constituent fragments. [ABSTRACT FROM AUTHOR]

Herbal medicine has been successfully applied in clinical therapeutics throughout the world. Following the concept of quantitative composition–activity relationship, the presented study proposes a computational strategy to predict bioactivity of herbal medicine and design new botanical drug. As a case, the quantitative relationship between chemical composition and decreasing cholesterol effect of Qi-Xue-Bing-Zhi-Fang, a widely used herbal medicine in China, was investigated. Quantitative composition–activity relationship models generated by multiple linear regression, artificial neural networks, and support vector regression exhibited different capabilities of predictive accuracy. Moreover, the proportion of two active components of Qi-Xue-Bing-Zhi-Fang was optimized based on the quantitative composition–activity relationship model to obtain new formulation. Validation experiments showed that the optimized herbal medicine has greater activity. The results indicate that the presented method is an efficient approach to botanical drug design. [ABSTRACT FROM AUTHOR]

Aminoguanidine and pyridoxamine (Pyridorin™), two major inhibitors of advanced glycation end product (AGE) formation, have entered clinical trials for diabetic nephropathy. They share no structural similarity and are believed to inhibit AGE formation by entirely different mechanisms. Pyridoxamine is a post-Amadori AGE inhibitor—that is, an "Amadorin"—whereas aminoguanidine primarily scavenges reactive dicarbonyl precursors to AGEs. However, pyridoxamine also has a limited potential to react with dicarbonyls. We thus embarked on an effort to develop second-generation Amadorins with low nucleophilicity. Our hypothesis was that we could improve specificity for inhibiting the post-Amadori pathway by minimizing the potential for scavenging small dicarbonyl intermediates. This mechanism-based strategy has led to a rational drug design program that has successfully produced candidate Amadorins, among them the novel compound BST-4997. This Amadorin has greater post-Amadori potency than pyridoxamine but possess no dicarbonyl scavenging activity. Prototypical inhibitors like BST-4997 provide a unique tool to help identify relevant AGE pathways that contribute to diabetic complications. Targeting AGE inhibition differs significantly from traditional approaches to drug discovery and thus represents a new paradigm for the drug industry that should be recognized. [ABSTRACT FROM AUTHOR]

Protein-substrate interactions in enzymatic, neurological, and immunological systems are typically characterized by a high degree of stereoselectivity towards complex substrates. We propose a novel stereocenter-recognition (SR) model for stereoselectivity of proteins (or receptors in general) towards substrates that have multiple stereocenters, based on the topology of substrate stereocenters. The model provides the minimum number of substrate locations that need to enter into binding, nonbinding, or repulsive interactions with receptor sites, for stereoselectivity to occur. According to this model, a substrate location may interact with multiple receptor sites, or multiple substrate locations may interact with a single receptor site, but a stereoselective receptor has to offer, in the correct geometry, at least as many interactions as the required minimum number of substrate locations. The SR model predicts that stereoselectivity towards an acyclic substrate with N stereocenters distributed along a single chain requires interactions involving a minimum of N + 2 substrate locations, distributed over all stereocenters in the substrate, such that effectively three locations exist per stereocenter. Thus, enantioselective recognition of molecules with one chiral center requires a protein to interact with a minimum of three substrate locations, while stereoselectivity towards substrates with two or three stereocenters requires interactions with a minimum of four or five substrate locations, respectively, and so on. We demonstrate the general applicability of this model to protein-substrate interactions by interpreting several previous experimental observations. [ABSTRACT FROM AUTHOR]

Histone deacetylase 6 (HDAC6) is unique hydrolase within HDAC family, having pleiotropic deacetylase activity against α‐tubulin, cortactin and dynein. Comprehensively, HDAC6 controls cell motility, apoptosis and protein folding, whereas alterations in its structure and function are related to the pathogenesis of cancer, neurodegeneration and inflammation. To define structural motifs which guide HDAC6 selectivity, we developed and compared three‐dimensional Quantitative Structure‐Activity Relationship (3D‐QSAR) models for HDAC1 and HDAC6 inhibitors. The reduction of the bias in conformer generation was supported by virtual docking study by using crystal structures of human HDAC1 and HDAC6 isoforms. Following these findings, the combined ligand‐based and fragment‐based drug design methodologies were used in the design of selective HDAC6 inhibitors. Group of the most promising novel ligands was selected based on the predicted HDAC6 selectivity, pharmacokinetic profile, synthetic tractability, and in silico cytotoxicity against the wide range of human cancer cell lines. [ABSTRACT FROM AUTHOR]